The global financial sector is characterized by a dynamic environment continuously adapting to changes in industry regulation, technological innovation, and shifting economic landscapes. The success of financial institutions, such as banks, lies in how best they manage the numerous risks and returns that shape their business operations. The 2008 financial crisis, which devastatingly rocked the global financial industry, exposed the need for stringent risk management and regulatory capital requirements in the sector. To thrive in the tumultuous financial sector, financial institutions have to continuously revise and update their risk management capabilities and align them with their business models.
Risk Management Trends
The contemporary financial industry has several risk management trends aimed at avoiding financial crises. Several financial institutions have adopted the use of data to support insight-driven risk management. Financial institutions, such as banks and insurance companies, invest heavily in advanced cognitive analytics to generate reliable risk management strategies (Paté‐Cornell, 2012). Moreover, the data generated from the use of advanced analytics is used for regulatory reporting purposes as they are accurate in predicting potential risks for financial institutions. According to Baxter et al. (2013), financial institutions utilize data for customer retention and predictive lending practices. Through advanced data analytics, financial institutions are able to fortify and monetize their proprietary data (Baxter et al., 2013). The usage of advanced data analytics provides a unique competitive advantage to financial institutions. Notably, apart from generating accurate risk management strategies, they can also monetize their data by selling it.
Digital audit, compliance, and risk monitoring and testing is another risk management trend in the contemporary financial industry. Numerous leading financial institutions have bolstered their risk management capabilities by adopting digital capacities to streamline their audit, compliance testing, and surveillance environments (Baxter et al., 2013). Digital audit compliance, and risk monitoring and testing capabilities have enabled large firms to minimize operation costs associated with hiring several risk management personnel. Moreover, the switch to digital risk management capacities has allowed financial institutions to have real-time access to well-analyzed information that is essential for decision making.
The adoption of digital risk management capabilities by several financial institutions has led to the proliferation of data privacy programs in the financial industry. The increasing usage of technology by several finance companies has led to an increase in cybercrime, such as data theft and data breaches. To counter cybercrime in the financial industry, numerous financial institutions have adopted data protection programs (Pakhchanyan, 2016). Moreover, financial regulations on privacy laws have also been enacted to ensure that financial institutions take proactive steps in protecting their customer’s data.
Risk Management Requirements
The financial industry has several risk management requirements due to its central role in both the national and global economic environments. Credit risk is a major part of the operations of several financial institutions as they make profits by extending loans to borrowers. To mitigate credit risks, financial institutions are required to establish prudent credit analysis and portfolio management of all their potential borrowers before providing them with loans (Andersen et al., 2012). Financial institutions also make profits by spreading interest rates between asset yields and liability costs. The difference in interest rate sensitivities between financial assets and liabilities gives rise to interest rate risk (Andersen et al., 2012). To manage interest rate risk, financial institutions must establish appropriate asset and liability management and hedging programs (Andersen et al., 2012). The asset and liability management and hedging programs enable financial institutions to create a positive and stable interest rate spread uniformly throughout numerous interest rate cycles. Financial institutions also generate income from financial services, such as cash management and securities processing. These financial services pose operational risks that have to be managed to ensure prudent cash and securities movement, record keeping, and adept logistics support.
The wide range of financial services offered by the financial industry means that there are numerous risk management requirements that financial institutions must adhere to. For example, insurance companies incur actuarial risks whenever they issue insurance policies that may result in colossal claims in the future (Andersen et al., 2012). Since the issuance of insurance policies is the chief source of income for insurance companies, they are required to manage their actuarial risks by closely monitoring the relationship between premiums earned and claims paid. Moreover, since insurance companies invest their initial profits, they are also required to manage their insurance portfolio. Financial institutions also have to manage cross-sector risks by monitoring default and counterparty risks (Andersen et al., 2012). Monitoring default and counterparty risks arise from lending activities, trading and settlement processes, and derivatives transactions (Andersen et al., 2012). The monitoring default and counterparty risks can be mitigated through the use of adequate credit exposure and credit rating system.
Risk Management Practices
Comprehensive risk assessment and management is one of the best risk management practices in the financial industry. Various organizations only rely on risk-assessment heat maps to determine their organization’s vulnerabilities (Paté‐Cornell, 2012). Though useful, the assessment heat maps do not enable a company to understand the root causes of the risks it faces. Comprehensive risk assessment and management allows a company to not only identify but also understand the risk content and trends behind risks facing the organization (Paté‐Cornell, 2012). By incorporating various factors, such as the root cause of risks, likelihood, and impact of a negative event, organizational preparedness, and trajectory of risks comprehensive risk assessment and management enables a company to fully assess, understand, and prepare for projected risks (Paté‐Cornell, 2012). Comprehensive risk assessment and management enables financial institutions to pinpoint risk areas based on each of the specific circumstances of the company and its resources.
Enterprise risk management (ERM) forms an integrated risk management practice in the financial industry. ERM is a strategy that focuses on the identification, assessment, and preparation for any potential negative outcome or hazard that may impact the operations of a business (Baxter et al., 2013). ERM is an integral part of risk management in the financial industry, particularly in the capital markets, and is also widely used by several other industries, such as construction, international development, and public health. The ERM approach espouses the collective assessment and management of risks and the provision of a wide range of management policies, limits, and procedures (Pakhchanyan, 2016). ERM enables shareholders’ and investors’ involvement in companies’ risk management practices through the publication of annual reports.
Use of Technology in Risk Management
Technological advancements have led to the integration of information technology (IT) into risk management, particularly in the financial industry. Several financial institutions and experts have predicted the fusion of IT into mainstream risk management in the financial sector. Pakhchanyan (2016) argues that the use of various forms of IT, such as advanced information analytics and artificial intelligence (AI), is already being used by several financial institutions to collect data for risk management purposes. Paté‐Cornell (2012) argues that the improved IT spending in the financial industry is an indicator of the future infusion of IT and risk management to produce better risk assessment outcomes in the turbulent financial sector. I opine that various forms of technology are already being utilized to improve risk management capabilities in the financial sector, and this trend is set to continue in the future.
The use of technology for risk management in the financial industry is feasible. Proper risk management requires the study and analysis of innumerable data, a process that can be made effective through the use of technology. Advanced analytics and AI, for example, can be used to analyze colossal amounts of data within a short time. Moreover, AI due to its ability to process large amounts of data during risk management processes, managers and organizations can use this form of technology to accurately predict potential risks and come up with effective measures to mitigate the consequences of such risks. Thus, by adopting the use of advanced analytics and AI, risk management within an organization can reach a better level and can support effective decision-making for senior management.
Success in the financial industry relies on the proper management of risks. The dynamic nature of the financial sector also requires financial institutions to be continuously on the lookout for potential risks and mitigate them before they incur losses. Risk management practices, such as ERM and comprehensive risk assessment and management, are essential in the detection and mitigation of potential risks in the financial industry. The use of technology for risk management offers immense opportunities for risk mitigation in the financial sector.
Andersen, L. B., Häger, D., Maberg, S., Næss, M. B., & Tungland, M. (2012). The financial crisis in an operational risk management context—A review of causes and influencing factors. Reliability Engineering & System Safety, 105, 3-12. https://doi.org/10.1016/j.ress.2011.09.005
Baxter, R., Bedard, J. C., Hoitash, R., & Yezegel, A. (2013). Enterprise risk management program quality: Determinants, value relevance, and the financial crisis. Contemporary Accounting Research, 30(4), 1264-1295. https://doi.org/10.1111/j.1911-3846.2012.01194.x
Pakhchanyan, S. (2016). Operational risk management in financial institutions: A literature review. International Journal of Financial Studies, 4(4), 20. https://doi.org/10.3390/ijfs4040020
Paté‐Cornell, E. (2012). On “Black Swans” and “Perfect Storms”: risk analysis and management when statistics are not enough. Risk Analysis: An International Journal, 32(11), 1823-1833. https://doi.org/10.1111/j.1539-6924.2011.01787.x